The Jack Mackerel Management Strategy Evaluation (MSE) Technical Workshop brought together scientists, technical experts, and external reviewers to review recent progress and refine the MSE framework being developed under SPRFMO. The primary goal of the workshop was to ensure that the modeling framework and management procedures (MPs) are scientifically sound, technically transparent, and aligned with management priorities.
MSE Framework Consolidation
Participants reviewed the jmMSE software package, confirming that it provides a robust and flexible platform for conducting MSEs. The package includes a reference set of operating models conditioned to historical data using MCMC, an efficient MP tuning algorithm, and tools for visualizing and comparing results.
Robustness Testing
The workshop clarified the role and scope of robustness tests. These tests are intended to explore how CMPs perform under a range of plausible yet uncertain scenarios rather than represent definitive alternative models. Scenarios reflecting changes in recruitment, spatial availability, environmental regime shifts (e.g., El Niño), and stock structure were reviewed and refined for implementation.
Indicator-Driven MPs and HCR logic
Empirical MPs based on one or more indicators were evaluated, with focus on two formulations:
We noted that the high current stock status (well within the “green” zone) tended to increase catch levels when tuning to achieve a desired P(Green). This can result in declining stock trends later in the projection period, even when the short-term performance criteria are met.
Recommendations and Refinements
The group recommended additional diagnostics and refinements, including:
Documentation and Transparency
The group emphasized the importance of transparency in documenting model assumptions, data sources, and MP structure. The group agreed on priorities for improving documentation and sharing annotated examples of MP behavior.
Next Steps and Implementation
The next phase of work will focus on finalizing the candidate MPs, running the robustness tests, and summarizing trade-offs across key performance indicators. In discussions we also identified future reporting needs, including summary tables and figures for managers, and exploration of reference points and evaluation criteria beyond the green zone probability.
Management Strategy Evaluation (MSE) has emerged as a critical tool for fisheries management, especially in contexts where data are limited or uncertainty is high. Foundational software frameworks like FLR were developed to facilitate reproducible, cross-disciplinary evaluation of management strategies through simulation and decision analysis (Laurence T. Kell and Scott 2007). Building on this foundation, recent advances have expanded FLR’s capacity for data-rich and data-limited systems alike, improving accessibility and integration with other tools (Hillary et al. 2023). Complementing these developments, a structured framework for evaluating methods and risk in data-limited fisheries has been proposed, providing practical guidance on the application of MSE in real-world settings (Carruthers et al. 2023).
The SCW15 Jack Mackerel MSE Technical Workshop was convened in response to the Scientific Committee’s request for progress on developing and evaluating management procedures (MPs) for jack mackerel under the SPRFMO framework. The meeting was held over five days (14–18 July 2025) and hosted in a hybrid format, with active participation from in-person attendees in Seattle and remote collaborators from SPRFMO Member States and invited experts. This event followed on previous technical work, including the SCW14 benchmark, and focused on finalizing the reference set of operating models (OMs), implementing robustness tests, and refining MP candidates using the jmMSE software package.
Throughout the week, participants engaged in live coding sessions, software validation, model tuning, and scenario refinement. The agenda was intentionally flexible, allowing the group to respond dynamically to technical challenges—such as issues with index generation, selectivity artifacts, and catch variability under different MP formulations. The workshop emphasized transparency, reproducibility, and documentation, with clear objectives to improve the utility and credibility of the MSE outputs ahead of Scientific Committee and Commission review.
By way of review, we provide a general outline for the workflow for defining and evaluating Management Procedures (MPs). We divided the process into three main stages (Figure 1).
The workshop participants recognized that all of the pieces for this exercise were available and implemented. However, the group struggled with getting the candidate MPs defined relative to available indices (“stage 1” in the diagram).
The appendices provide the participants, the agenda and the daily activities in the workshop minutes. The review from the external experts is summarized in an appendix as well.
The following sections detail the discussion on how best to incorporate some environmental effects for projecting from the operating model. Specifically, we considered how to account for the effects of El Niño on recruitment and catchability/availability. We follow this with a sections reviewing the OM and then some results from applying existing and refined MPs. We conclude with a set of recommendations for the SC to consider.
To incorporate climate-driven variability into the Operating Model (OM) projections, we defined a scenario simulating El Niño–like events every five years beginning in 2030. These events affect primarily recruitment and distribution options. Based on the work of Iago and available literature, we proposed biological and fishery processes that may relate to El Niño conditions. The group discussed these and noted that they could be evaluated in the next round of stock assessment benchmark and for future MSE work.
The table below summarizes the proposed effects of simulated El Niño conditions on the OM, categorizing them by their expected direction, biological or fishery-based justification, and evaluation priority. The table is divided into two sections: effects that are prioritized for immediate evaluation and those deferred for further study.
The first items highlights two key El Niño-driven effects: A 30% increase in recruitment with a one-year lag, linked to ENSO-related early life stage survival (Figure 2). Shifts in catchability, with coastal regions experiencing increased availability and offshore regions seeing declines, reflecting observed onshore movement of fish during warm anomalies. This is also high-priority, with a focus on quantifying impacts on fishery removals.
The deferred effects were discussed and included the potential for reduced weight-at-age (potentially due to prey scarcity), earlier maturity (a stress response observed in small pelagics), and increased natural mortality (from predation or environmental stress). These are flagged for future study, pending historical data checks or further evidence. The table succinctly organizes hypotheses while clarifying immediate next steps for the OM framework.
The following tables summarizes the key effects, their expected directions, and justification.
| Effect | Direction | Justification |
|---|---|---|
| Recruitment ↑20% | ↑ 1-year lag | ENSO-linked early life stage effects on recruitment |
| Regional availability | Coast catchability ↑ and offshore ↓ | Onshore shift during warm anomalies |
Discussed but deferred for further study
| Effect | Direction | Justification | Notes |
|---|---|---|---|
| Weight-at-age | ↓Productivity | Lower prey density and observed condition declines | Check historical WAA anomalies |
| Age-1 maturity | Earlier maturity | Stress response seen in small pelagics | similar impact on Recruitment |
| Coastal selectivity | Age 1–2 sel ↑ | Spatial contraction, availability change | Confirm from CPUE by age? |
| M ↑30%/20% | ↓ Survival | Stress-induced mortality, predation |
Taking an assumption that over a recent period that changes in a smoothed proportion of catch by coastal and offshore areas roughly relates to the effective catchability \(q\) (which includes both true catchability and availability) of the fishery, i.e.:
\[ \text{Catch}_{\text{fleet}} \propto q_{\text{fleet}} \]
Thus, observed catch proportions can serve as a proxy for relative availability.
| Year Range | Coastal (%) | Offshore (%) |
|---|---|---|
| 2004–2008 | 81 | 19 |
| 2005–2009 | 78 | 22 |
| 2006–2010 | 74 | 26 |
| 2007–2011 | 76 | 24 |
| 2008–2012 | 78 | 22 |
| 2009–2013 | 82 | 18 |
| 2010–2014 | 84 | 16 |
| 2011–2015 | 86 | 14 |
| 2012–2016 | 86 | 14 |
| 2013–2017 | 85 | 15 |
| 2014–2018 | 86 | 14 |
| 2015–2019 | 87 | 13 |
| 2016–2020 | 91 | 9 |
| 2017–2021 | 93 | 7 |
| 2018–2022 | 94 | 6 |
| 2019–2023 | 94 | 6 |
| 2020–2024 | 94 | 6 |
| Mean | 85 | 15 |
Given this pattern we can assume a relative catchability due to an environmental effect. We note that the
As a sensitivity, we could propose that the effective availability to the offshore fleet drops from 15% of the biomass (the mean) to gradually to 6% during El Niño periods (a 60% decline in \(q\) ). This would apply to the data generated for offshore CPUE index in the simulations. For the coastal zones, the effect of El Niño would correspond to an 11% increase in the availability of fish relative to the mean (85%). These changes would apply to the Chilean SC CPUE index and the Peruvian CPUE index data generation. This is one proposal among many that could be imagined. For example a slightly more conservative range could be based on the 10th and 90th percentiles of estimated effective catchability (from proportional catches):
Coastal:
• 10th percentile: 77%
• 90th percentile: 94%
Offshore:
• 10th percentile: 6%
• 90th percentile: 23%These shifts may provide some scope for showing the impact of changes in the relative abundance indicators in index values. These reflect patterns over the past two decades, possibly due to environmental changes.
The workshop reviewed the current specifications of the OMs. In particular, the assumptions for the reference point calculations were discusssed and contrasted with the 2024 assessment results and reports (South Pacific Regional Fisheries Management Organisation (2024)). Due to the terminal (2024) estimates of fisheries selectivities, the assessment report had anomalously high values for \(F_{MSY}\)
While the 2024 stock assessment produced high estimates of potential catch under the third tier of the harvest control rule—exceeding 4,900 kt based on \(F_{MSY}\)—this result was considered unrealistic due to likely upward bias in \(F_{{MSY}}\) estimates caused by strong selection on older fish. As a result, the Scientific Committee recommended constraining the 2025 TAC to be at or below 1,428 kt, representing only a 15% increase from 2024 levels and aligned with the Commission’s guidance. In developing the OM, reference points such as \(F_{{MSY}}\) were instead based on longer-term averages to avoid the influence of short-term variability or cohort effects, ensuring more stable and precautionary management advice consistent with the MSE framework (Figure 3).
Several issues were identified with the current stock assessment that warrant further attention ahead of the next benchmark. Key among them are uncertainties in catch-at-age data stemming from differences in age determination methods across laboratories, as well as assumptions about mean body weight at age. The Scientific Committee emphasized the need for standardizing CPUE indices and improving data collection protocols, particularly regarding fleet-specific efficiency changes. Sensitivities to early age composition data—especially from the pre-1990 period—remain unresolved, with residual patterns noted for the North Chilean fleet. In addition, assumptions underlying selectivity and recruitment regimes were highlighted as critical sources of uncertainty, with substantial influence on reference points and management advice. Finally, the Committee underscored the importance of continued evaluation of single-stock versus two-stock model structures using simulation and MSE tools.
The SCW15 workshop provided a venue for progressing the Jack Mackerel MSE work, resolving some technical issues, and evaluating multiple MP configurations. A key outcome was the identification of problems in how MPs interacted with the OMs. This raised the need for either resolution of MP specification issues prior to the SC and/or holding a separate follow-up technical meeting, ideally in person. This would focus discussion on narrowing MP options. Depending on this direction, it may mean that such a meeting would have to occur after February 2026 and the Commission meeting.
Continue using FLR as the main MSE engine unless there is a dedicated effort to migrate to openMSE or another platform.
Improve naming conventions in code to reduce ambiguity. For example:
Functions like cpuescore2.ind and cpuescore3.ind are not intuitive.
The target argument is overloaded: it may refer to either a TAC or depletion target depending on context.
The group noted that MSE funding (in the form of providing support from external developers) may be available but would be contingent on:
Coordination with the current analyst (Iago).
Collaboration with the technical team.
Openness to using openMSE.
Clear timelines and deliverables.
Proposed deliverables and deadlines include:
Reference OMs (no multistock): End of July
Robustness OMs (no multistock): End of August
Shortcut calibration to the JJM assessment: End of August
Range of shortcut MPs run for all reference OMs: End of July
Planned products:
Technical documentation and reports:
Draft Technical Summary Document (TSD) by end of July.
Technical working papers and presentations for:
Shortcut calibration to JJM
Reference set OM results for MP archetypes
Robustness OM results
MP performance summaries
Slick MSE results summary.
TSD v1 by end of September.
Adopt the current proposal structure with flexibility for future adjustment.
Recommend a shortlist of MP options to simplify the selection process at the Commission level.
Consider a placeholder method for calculating the 2026 TAC if MSE work is not yet finalized.
Commit to a shared MSE software base (FLR or openMSE).
Engage in pre-SC online meetings to broaden participation in MSE discussions.
Prioritize enhancements discussed during the workshop:
Code clarity and naming conventions
Logical parameter usage across MPs
Refinement of FLR-to-dataframe functions
Identify successor strategy after contract ends in 2025.
Participants were encouraged to document their activities during the workshop, including the methods explored and tuning targets used. Work tasked identified included:
Jim evaluated 9 MPs (including bufferdelta2, cpuescore2, test acoustic, and combinations of CPUE indices with different delta_TAC values), all tuned to achieve 60% green status.
Jose/Chile apply shortcut tuning methods to reach similar green zone targets.
| Year | Method | Metric | Tuning Parameter | Other Parameters | Score Index | Comments |
|---|---|---|---|---|---|---|
| 2024+ | buffer.hcr |
depletion | target | bufflow, buffup, limit | cpuescore3.ind |
Original pkg function |
| 2024+ | bufferdelta.hcr |
depletion | width | sloperatio | cpuescore3.ind |
Modified; not compatible with z-score metrics |
| 2024+ | bufferdelta2.hcr |
zscore | width | sloperatio | cpuescore2.ind |
New; not compatible with depletion |
| 2024+ | buffer2.hcr |
zscore | target | width (affects buffer) | cpuescore2.ind |
Original; adjusted for zscore (limit = -2 SD) |
Welcome and Introduction
Objective: Set the stage for the focused discussion on Jack Mackerel MSE progress and future directions.
Note: Briefly introduced the MSE as a tool for sustainable fisheries management, emphasizing its importance given the historical fluctuations in jack mackerel stock and exploitation levels.
Current Status of Jack Mackerel MSE Work
Objective: Provide a concise update on progress, including advancements in OMs and MP testing.
Note: MSE development has progressed rapidly. Updates include new data inputs, refinements from the SCW14 benchmark, and expanded uncertainty axes. MPs are under active testing.
Review of Candidate MPs and Tuning Results
Objective: Discuss MP structure, indicator choices, and implications of tuning to achieve target performance criteria.
Note: Focused on empirical MPs using CPUE and acoustic indices. Tuning challenges under high biomass conditions were highlighted.
Robustness Scenarios and Specification Refinement
Objective: Finalize the list of scenarios for robustness testing.
Note: Scenarios included El Niño-like variability, availability shifts, and alternative stock structures. Their role as comparative stress tests was reaffirmed.
Evaluation Metrics and Visualization Tools
Objective: Review tools for comparing MP performance.
Note: Emphasis on visual summaries, including Kobe plots, probability tiles, and trade-off diagrams.
Feedback from External Experts
Objective: Integrate external review findings and technical suggestions.
Note: Dr. Parma’s input emphasized realistic assumptions, consistent reference points, and long-term performance evaluation.
Wrap-up and Next Steps
Objective: Identify action items and prepare for upcoming reporting deadlines.
Note: Plans were set for refining MPs, running full simulations, and summarizing results in a format accessible to decision-makers.
The workshop included both in-person and online participation, with representatives from Peru, Chile, Argentina, Ecuador, and the Netherlands, among others.
The agenda was described as ambitious, with a focus on hands-on technical work, including software installation and repository access.
Two main GitHub repositories are central: FLjjm (for building FLR objects and running JJM inside the MP) and jjmMSE (the main development site for the MSE work).
The workflow follows the DAF (Data, Analysis, Framework) system, with clear steps for data preparation, OEM (Operating Model) conditioning, and performance analysis.
Emphasis was placed on forking repositories and using branches for collaborative work, with a preference for merging at the end of the week.
Docker was suggested as a potential solution for ensuring consistent environments across operating systems and participants, though not yet implemented.
The group is working with both single-stock and two-stock hypotheses, including a two-stock model with future-applied connectivity based on movement matrices.
Robustness scenarios are being explored, including cyclic environmental changes and their impacts on productivity.
The group discussed the use of “.q” files for efficient storage and handling of large simulation outputs.
The calculation and interpretation of MSY (Maximum Sustainable Yield) and FMSY were debated, with concerns about the realism of current methods in JJM.
It was agreed that the 10-year average of MSY reference points would be used for performance evaluation, to avoid short-term volatility.
Selectivity patterns and their impact on projections were a major topic. The group considered transitioning from terminal year selectivity to long-term averages over a five-year period to avoid unrealistic jumps in catch projections.
There was consensus that the main focus should be on long-term performance of management procedures, but short- and mid-term results are also important for managers.
The workshop addressed the need to model environmental variability, particularly El Niño events, and their impact on stock productivity, weight-at-age, and selectivity.
Literature-informed scenarios were presented, with parameters for changes in mortality, recruitment, and spatial distribution.
Regional differences (e.g., between far north and south stocks) and their implications for catchability and biological responses were discussed.
Participants will review and potentially refine the environmental scenarios, with a focus on realism and literature support.
Further work is planned on selectivity transitions and the technical implementation of gradual changes.
The group will continue to test and validate the workflow, with an emphasis on reproducibility and collaborative code development.
The workshop began with participant introductions, including new attendees such as Robert Robinson.
Jim Ianelli welcomed participants, provided a recap of the previous day, and referenced a summary posted in the Teams channel for review.
The agenda was described as ambitious, with a focus on technical aspects of Management Strategy Evaluation (MSE).
Discussion included the effects of El Niño on recruitment and catch distribution, referencing an unsent summary email and a report on the topic.
Jim presented an analysis of catch distribution changes between coastal and offshore fleets, proposing a 15% average offshore drop (60% decline in Q for offshore fleet) and an 11% increase for coastal zones.
Participants debated the appropriateness of using long-term versus short-term averages and the need for smoothing changes rather than step changes.
It was agreed that the effect should be applied to both availability and catchability in simulations, with implications for the acoustic survey indices.
The group discussed the transition from recent selectivity estimates to long-term means, with concerns about artifacts from using 10-year averages.
Consensus moved toward using a representative period (2000–2010) for selectivity, avoiding recent years with anomalously high selection for older fish.
The need for a smooth transition in selectivity assumptions for projections was emphasized.
Analysis of recruitment means for projections considered the El Niño effect, with a proposed 23–30% bump up in recruitment for recent years.
Debate ensued on which years to include for calculating means, with a focus on data consistency from 1991 onward.
The group discussed the potential for regime shifts and the implications for robustness testing in MSE.
Demonstrations were given on the use of R and package structures for running MSE simulations, including best practices for project setup and function sourcing.
The importance of consistent selectivity and Q parameter normalization across projections and indices was highlighted.
Adopt 2000–2010 as the reference period for selectivity in projections, with a smooth transition from current conditions.
Apply a 23–30% recruitment increase for projections reflecting recent El Niño effects, with final years to be confirmed.
Ensure normalization of selectivity and Q parameters is consistent across all indices and projections.
Continue refining the codebase, documenting changes, and sharing updates among the technical team.
Participants shared experiences with data handling, model setup, and coding challenges.
The workshop included informal discussions and technical clarifications.
The session concluded with plans to continue reviewing model outputs and performance indicators, and to reconvene as needed for further technical work.
Discussion focused on the technical aspects of Management Strategy Evaluation (MSE) for jack mackerel.
Participants examined the function and configuration of sliding buffers, control rules, and the impact of selectivity changes.
The group reviewed how indices (e.g., CPUE, acoustics) are incorporated, including the use of averages over multiple years and weighting schemes for recent data.
There was debate on the variance and correlation structure in observation models, and how these affect simulation results.
The workflow and timing for implementing MPs were clarified:
Data from 2026 would be used for advice in 2027.
The Scientific Committee (SC) would run the MP, with the Commission making final decisions.
Discussion on the need for preliminary versus finalized data and the implications for observation error.
The importance of using the most recent data versus the stability of multi-year averages was highlighted.
Participants agreed that simulating effort creep in future projections is not necessary for the base case, but could be a robustness test.
The importance of correcting historical indices for effort creep was emphasized, while future indices are assumed to be unbiased.
There was consensus that the standardized or corrected index should be used for both simulation and real-world application.
Multiple MPs were tested, each tuned to a 60% probability of meeting the Kobe green zone.
The group compared different buffer widths and TAC change limits, analyzing their effects on catch variability and performance metrics.
Discussion included the need for clear documentation of specifications and the importance of presenting a set of MPs with distinct trade-offs to the Commission, rather than recommending a single option.
The team reviewed performance across near-term, medium-term, and long-term projections.
Boxplots and other visualizations were used to compare MPs, focusing on catch variability, probability of stock being in the green zone, and interannual TAC changes.
Concerns were raised about downward trends in some simulations and the need for additional performance statistics (e.g., probability of stock crash).
Agreement to refine the OM projections and finalize Annex documentation on selectivity and other specifications.
Plan to continue testing and tuning MPs, with further analysis of performance metrics.
The SC will present a set of MPs to the Commission, along with the status quo as a fallback.
Lunch arrangements and informal discussions concluded the session.
Model Runs and Debugging:
Overnight and morning runs were conducted to examine the acoustics index using legacy targets and buffers.
A significant focus was on debugging the generation of CPUE v3 for 2024 and 2025, investigating unexpected jumps in predicted values.
Alternative normalization methods for CPUE were tested, following recommendations to improve stability.
Analysis of Index Jumps:
The team identified that the jump in the index from 2024 to 2025 was primarily driven by increases in vulnerable biomass and changes in mean weight at age, rather than selectivity changes alone.
Weighted age calculations and their implications for projections were reviewed in detail, including the use of three-year means and the impact of preliminary data from 2024.
Code Review and Live Demonstration:
Live coding sessions were held to demonstrate how to adjust the OM to exclude problematic years (e.g., dropping 2024 and extending from 2023).
Smoothing techniques for selectivity and weights at age were discussed and implemented to reduce artificial jumps in projections.
Uncertainty and Robustness:
The group discussed the treatment of process error, residual variability, and autocorrelation in indices.
Empirical approaches to setting CVs for indices were compared with default values, and the impact on future projections was considered.
Action Items and Next Steps:
Further testing of the OM with adjusted years and smoothing is to be completed, with outputs to be pushed under new filenames to avoid disrupting ongoing work.
Continued analysis of the causes of high catches in test runs and further parameter tuning were assigned.
The group agreed to revisit and possibly refine the approach to handling preliminary data and smoothing in both indices and weights at age.
Consensus that both selectivity and mean weight at age contribute to observed index jumps, with smoothing and exclusion of problematic years being viable mitigation strategies.
Agreement to document and communicate technical progress to the broader group, while maintaining a focus on robust, transparent modeling practices.
The group reviewed progress on running various management procedures (MPs), focusing on the acoustic and CPUE indices. Issues with FL libraries and model reproducibility were discussed, with fixes applied to ensure models ran as expected.
Robustness tests were conducted, particularly comparing the performance of different indices (acoustic, CPUE3, CPUE6, 3.6). The need to clarify how “shortcut” procedures treat stocks was debated, especially regarding biomass tracking and catch splits.
The group noted that tuning parameters (e.g., width, slope ratio) and their impact on model performance remain a challenge, especially when standardizing across indices.
There was significant discussion about interpreting outputs, particularly when catch trends did not align with biomass trends. Concerns were raised about the credibility of certain indices and the need for clearer communication of model behavior.
The importance of visualizing trade-offs and the response of TAC to indices was emphasized, with ongoing efforts to develop summary figures for inclusion in reports.
Participants shared experiences with the codebase, noting progress in understanding and modifying functions, but also highlighting the need for further documentation and standardization.
The group agreed on the value of reproducibility and transparency, with suggestions to document daily progress and maintain clear records for future reference.
The group recognized that while technical progress was made, the process is ongoing. There was consensus on the need for another technical workshop (ideally in person) to continue development and evaluation of MPs.
The timeline for delivering a report to the Scientific Committee (SC) and Commission was discussed, with acknowledgment that final recommendations are not yet possible. Instead, the report will focus on documenting progress, challenges, and a proposed work plan for the coming year.
Concerns about funding, continuity (especially regarding software and contracts), and the need for member commitment to ongoing tool development were raised.
The group agreed to recommend continued development and evaluation of MPs, with an emphasis on transparency, reproducibility, and clear communication to managers.
It was noted that, if a new MP is not ready for 2026, the existing method (Annex K) will remain in use.
There was broad recognition of the complexity and time required for this process, and appreciation for the collaborative progress made during the workshop.
Dr. Ana Parma, along with Qi Lee reviewed the jmMSE framework and its application to evaluating candidate management procedures (CMPs) for jack mackerel. Their comments provide both a validation of the current approach and targeted suggestions for improvement.
The jmMSE package offers a comprehensive and flexible platform for conducting Management Strategy Evaluation (MSE). It includes:
A variety of robustness tests were developed to address key uncertainties identified for jack mackerel, such as recruitment variability, fleet-specific availability, and stock structure assumptions (one vs. two stocks). Many of these assumptions relate to potential impacts from El Niño-like events. These robustness scenarios were refined during the workshop, and their role was clarified as stress tests—designed not to predict specific mechanisms, but to evaluate the relative performance of CMPs under plausible alternative futures.
Two primary classes of empirical MPs were evaluated:
During tuning (e.g., to achieve P(Green) = 0.6), both approaches frequently led to increased TACs and eventual stock declines—especially under a high current stock status. This points to the need for additional metrics that reflect long-term sustainability, not just near-term status probabilities.
Some actionable recommendations were articulated:
This document summarizes the structure and behavior of key objects used in the jmMSE Management Strategy Evaluation (MSE) framework for SPRFMO Jack Mackerel. It documents the modeling components (h1, om, perf), performance calculations, and the use of getSlick() and FLslick() to generate evaluation plots.
mpCtrl and mseCtrlIn the mse package, Management Procedures (MPs) are constructed as modular sequences of functional components that simulate how a fishery would be managed under alternative strategies. These strategies are defined using mpCtrl, which organizes component modules defined via mseCtrl.
This modular design allows you to:
Select estimation methods for stock status (est)
Define harvest control rules (hcr, phcr)
Simulate implementation systems (isys)
Include optional technical measures (tm)
mpCtrlThe mpCtrl() constructor takes a named list of components. Each component must be an mseCtrl object that defines the function to use (method) and its input parameters (args).
ctrl <- mpCtrl(list(
est = mseCtrl(method = shortcut.sa, args = list(
metric = "depletion",
devs = metdevs,
B0 = refpts(om)$SB0
)),
hcr = mseCtrl(method = buffer.hcr, args = list(
target = 1000,
bufflow = 0.30,
buffupp = 0.50,
lim = 0.10,
min = 0,
metric = "depletion"
)),
isys = mseCtrl(method = split.is, args = list(
split = catch_props(om)$last5
))
))| Component | Description |
|---|---|
est |
The estimator module determines how the stock status is assessed during each management cycle. |
hcr |
The harvest control rule uses the estimated status to recommend a total allowable catch or effort level. |
phcr |
(Optional) Parametrization helper for complex or adaptive control rules. |
isys |
Simulates how the recommended catch is implemented across fleets, often using historical proportions or allocation logic. |
tm |
(Optional) Includes non-catch-based rules, such as minimum size limits or gear restrictions. |
method and args Options| Component | Example method |
Common args |
|---|---|---|
est |
perfect.sa, shortcut.sa |
metric, devs, B0, years |
hcr |
buffer.hcr, hockeystick.hcr, trend.hcr |
target, lim, bufflow, buffupp, trigger, metric |
isys |
split.is, fixed.is |
split, noise, bias |
tm |
user-defined | size or spatial constraints |
mseCtrl for a Harvest Control RuleEach mseCtrl specifies a method function and its arguments.
ctl <- mseCtrl(
method = buffer.hcr,
args = list(
target = 1000,
bufflow = 0.30,
buffupp = 0.50,
lim = 0.10,
min = 0,
metric = "depletion"
)
)You can programmatically access or modify components of an mpCtrl object:
# Access or change the HCR method
method(ctrl@hcr)
# Update the target biomass value
args(ctrl@hcr)$target <- 1200Alternatively, use accessor functions:
hcr(ctrl) <- mseCtrl(method = new.hcr, args = list(...))
args(ctrl, "hcr")$target <- 1100This framework enables:
Rapid prototyping of management strategies
Transparent comparisons across MPs
Flexible integration with simulated operating models
By separating each component of an MP into a function-object pair, the mse package supports reproducible, configurable, and extensible MSE design workflows.
To analyze the behavior of bufferdelta.hcr() over the range of index values used as input (i.e., the stock status metric like “depletion” or “zscore”), the key output to examine is the harvest control multiplier hcrm—which determines how much the TAC is adjusted relative to the previous TAC. This multiplier is a piecewise function of the index value at the data year.
This HCR formulation uses a smoothed transition based on a buffer zone around a biomass or metric target. The response scalar \(h(m)\), applied to the previous catch, is defined based on the relative metric value \(m\) (e.g., standardized index or depletion level), and follows a piecewise logic:
Let:
Then the Harvest Control Rule (HCR) response multiplier \(h(m)\) is:
\[ h(m) = \begin{cases} \frac{1}{2} \left(\frac{m}{l}\right)^2, & \text{if } m \leq l \\ \frac{1}{2} \left(1 + \frac{m - l}{b_{\text{low}} - l} \right), & \text{if } l < m < b_{\text{low}} \\ 1, & \text{if } b_{\text{low}} \leq m < b_{\text{upp}} \\ 1 + r \cdot \frac{1}{2(b_{\text{low}} - l)} (m - b_{\text{upp}}), & \text{if } m \geq b_{\text{upp}} \\ \end{cases} \]
The resulting Total Allowable Catch (TAC) is calculated as:
\[ \text{TAC}_{\text{new}} = \text{TAC}_{\text{previous}} \cdot h(m) \]
Optional constraints on TAC changes can be applied:
\[ \text{TAC}_{\text{new}} = \min(\text{TAC}_{\text{new}}, \text{TAC}_{\text{previous}} \cdot d_{\text{upp}}) \]
\[ \text{TAC}_{\text{new}} = \max(\text{TAC}_{\text{new}}, \text{TAC}_{\text{previous}} \cdot d_{\text{low}}) \]
Let’s denote:
\(m\): index metric (e.g., depletion) at the data year
\(\text{target}\): central value, e.g., 0.5
\(\text{width}\): buffer width, e.g., 1.0
Then:
\(\text{bufflow} = \text{target} - \text{width}\)
\(\text{buffupp} = \text{target} + \text{width}\)
\(\text{lim} = \text{target} - 2 \cdot \text{width}\)
| Index value \(m\) | Description | Multiplier h(m) | TAC behavior |
|---|---|---|---|
| \(m \leq \text{lim}\) | Very low index | Quadratic increase from 0 → 0.5 | Strong reduction |
| \(\text{lim} < m < \text{bufflow}\) | Between limit and lower buffer | Linear rise from 0.5 to 1 | Moderate reduction |
| \(\text{bufflow} \leq m < \text{buffupp}\) | Within buffer zone | Flat at 1 | No change |
| \(m \geq \text{buffupp}\) | Above upper buffer | Linear increase starting at 1 (slope = sloperatio) | Moderate increase |
If you use the defaults:
target = 0.5
width = 1
sloperatio = 0.2
Then:
• bufflow = -0.5, buffupp = 1.5, lim = -1.5
• The flat zone is from -0.5 to 1.5 (note: with depletion metric, this wide range makes sense for standardized metrics like z-scores, but not raw depletion)If using depletion as the metric, you’d typically want:
target = 0.4
width = 0.1
sloperatio = 0.2
→ lim = 0.2, bufflow = 0.3, buffupp = 0.5 So the response curve looks like:
plot_hcrm <- function(target = 0.4, width = 0.1, sloperatio = 0.2, metric_range = seq(0, 1, 0.01)) {
bufflow <- target - width
buffupp <- target + width
lim <- target - 2 * width
hcrm <- ifelse(metric_range <= lim,
((metric_range / lim)^2) / 2,
ifelse(metric_range < bufflow,
0.5 * (1 + (metric_range - lim) / (bufflow - lim)),
ifelse(metric_range < buffupp,
1,
1 + sloperatio * 1 / (2 * (bufflow - lim)) * (metric_range - buffupp)
)
)
)
plot(metric_range, hcrm,
type = "l", col = "blue", lwd = 2,
xlab = "Metric (e.g., depletion)", ylab = "Harvest multiplier (hcrm)",
main = "Response of TAC to Index Metric"
)
abline(h = 1, col = "gray", lty = 2)
abline(v = c(lim, bufflow, buffupp), col = "red", lty = 3)
}
plot_hcrm(target = 0.4, width = 0.1, sloperatio = 0.2)
This shows the piecewise nature of the multiplier and can be tailored to any input metric (depletion, zscore, etc.).
In the jmMSE framework, different CPUE scoring functions are used to inform harvest control rules (HCRs). These functions standardize or compare CPUE time series across simulations and reference periods. The three primary scoring methods are:
cpuescore.zcpuescore.meancpuescore.levelcpuescore.zThis method standardizes the CPUE values by subtracting the mean and dividing by the standard deviation across simulations:
\[ \text{score}_{i,t} = \frac{\text{CPUE}_{i,t} - \mu_t}{\sigma_t} \]
Where:
\(\mu_t\) = mean CPUE in year \(t\) across simulations
\(\sigma_t\) = standard deviation in year \(t\) across simulations
This produces a unitless, centered score:
score <- (met[, ac(dy[i])] %-% yearMeans(ref)) %/% sqrt(yearVars(ref))Useful when you want to assess relative anomalies in CPUE from expected trends.
⸻
This method compares the mean CPUE in recent years (dy) to a reference mean CPUE: \[ \text{score}_i = \frac{\bar{\text{CPUE}}_{dy, i}}{\bar{\text{CPUE}}_{ref, i}} \] This is a relative index level and is not standardized:
score <- yearMeans(met[, ac(dy[i])]) %/% yearMeans(ref)Often used when absolute differences in mean CPUE should affect TAC decisions.
This method passes through the CPUE values with no transformation:
\[ \text{score}_{i,t} = \text{CPUE}_{i,t} \]
score <- met[, ac(dy[i])]Used when raw or smoothed CPUE indices are deemed directly interpretable and comparable.
The following example shows how the three cpuescore methods behave using simulated CPUE data across 100 simulations and 10 years.
library(dplyr)
library(tidyr)
library(ggplot2)
# Simulate CPUE data
set.seed(42)
n_sim <- 100
n_year <- 10
ind_sim <- matrix(rlnorm(n_sim * n_year, meanlog = log(1), sdlog = 0.2), nrow = n_sim)
# Reference: all years; "recent" period: years 9-10
ref_mean <- rowMeans(ind_sim)
ref_sd <- apply(ind_sim, 1, sd)
z_scores <- (ind_sim[, 10] - ref_mean) / ref_sd
mean_scores <- rowMeans(ind_sim[, 9:10]) / ref_mean
raw_scores <- ind_sim[, 10]
# Reshape for plotting
score_df <- tibble(
sim = 1:n_sim,
z = z_scores,
mean = mean_scores,
level = raw_scores
) %>% pivot_longer(cols = -sim, names_to = "score_type", values_to = "score")
ggplot(score_df, aes(x = sim, y = score)) +
geom_line() +
# facet_wrap(~score_type, scales = "free_y") +
facet_wrap(~score_type) +
labs(
title = "Simulated CPUE Score Types",
x = "Simulation",
y = "Score Value"
) +
theme_minimal()
The indices were refactored from the cpuescore functions to avoid confusion with surveys and can be summarized as follows:
| Function | Description | Normalized | Use case |
|---|---|---|---|
indscore.z |
Standardize vs mean/sd | ✅ | Compare anomalies across sims |
indscore.mean |
Mean ratio of dy vs ref | ❌ | Index levels matter |
indscore.level |
Raw CPUE values used as-is | ❌ | When index is well-calibrated |
⸻
indscore.z <- function(
stk, idx, index = 1, dlag = rep(args$data_lag, length(index)),
refyrs = NULL, args, tracking) {
dlag <- setNames(dlag, nm = names(idx)[index])
ay <- args$ay
dy <- ay - dlag
res <- as.list(setNames(nm = names(idx)[index]))
for (i in names(res)) {
met <- window(idx[[i]], end = dy[i]) # removed biomass()
ref <- if (!is.null(refyrs)) met[, ac(refyrs)] else met
res[[i]] <- (met[, ac(dy[i])] %-% yearMeans(ref)) %/% sqrt(yearVars(ref))
dimnames(res[[i]])$year <- max(dy)
track(tracking, paste0("score.mean.", i), ac(ay)) <- yearMeans(ref)
track(tracking, paste0("score.sd.", i), ac(ay)) <- sqrt(yearVars(ref))
track(tracking, paste0("score.ind.", i), ac(ay)) <- res[[i]]
}
return(list(stk = stk, ind = res, tracking = tracking, cpue = met))
}
bufferdelta.hcr <- function(stk, ind, target = 0.5, width = 1,
sloperatio = 0.2, dupp = NULL, dlow = NULL,
args, tracking) {
# setup
ay <- args$ay
iy <- args$iy
frq <- args$frq
man_lag <- args$management_lag
cys <- seq(ay + man_lag, ay + man_lag + frq - 1)
# compute score
score <- ind[[1]] # assuming single FLQuant named 'zscore', 'mean_ratio', or 'level'
# define slope
bufflow <- target - width
buffupp <- target + width
lim <- target - 2 * width
slope <- sloperatio / width
# compute h(score)
h <- ifelse(score < lim, 0,
ifelse(score < bufflow,
slope * (score - lim),
ifelse(score > buffupp, 1 + sloperatio * (score - buffupp), 1)
)
)
tac_prev <- catch(stk)[, ac(iy - 1)]
tac_new <- tac_prev * h
# apply TAC limits if given
if (!is.null(dupp)) tac_new <- pmin(tac_new, tac_prev * dupp)
if (!is.null(dlow)) tac_new <- pmax(tac_new, tac_prev * dlow)
catch(stk)[, ac(cys)] <- tac_new
return(list(stk = stk, ind = ind, tracking = tracking))
}
args <- list(ay = 2025, iy = 2026, frq = 1, data_lag = 1, management_lag = 1)
idx <- list("cpue" = FLQuant(rlnorm(30), dimnames = list(year = 1996:2025)))
stk <- FLStock(catch = FLQuant(1000, dimnames = list(year = 2025:2030)))
result <- indscore.z(stk, idx, args = args, tracking = FLQuant(0))
output <- bufferdelta.hcr(result$stk, result$ind, args = args, tracking = result$tracking)
catch(output$stk)
args <- list(ay = 2025, iy = 2026, frq = 1, data_lag = 1, management_lag = 1)
idx <- list("cpue" = FLQuant(rlnorm(30), dimnames = list(year = 1996:2025)))
result <- indscore.z(stk, idx, args = args, tracking = FLQuant(0))
output <- bufferdelta.hcr(result$stk, result$ind, args = args, tracking = result$tracking)| Object Name | Type / Purpose |
|---|---|
h1 |
A list containing the full OM, OEM, and IEM for hypothesis H1 (qs file) |
om |
Iterated subset of the Operating Model from h1 |
oem, iem |
Observation and implementation error models; extracted from h1 |
omperf |
Performance metrics of OM alone, usually C, F, SB for conditioning years |
perf |
Combined data frame of MP simulation performance results |
getSlick |
Function that merges MP/OM results and constructs a Slick summary object |
FLslick |
Constructor function that builds and returns a Slick object for plotting |
sli |
The returned Slick object for visualization (Kobe, Quilt, Spider, etc.) |
ctrl |
A list of control parameters for MPs (e.g., estimation methods, tuning devs) |
condition |
Not found in current project files; possibly a misidentified object |
plot_slick_quilt(sli, stat = "longterm C")
plot_slick_spider(sli, om_idx = 1, mp_idx = 2)
plot_slick_tradeoff(sli, stat = "PSBMSY")mpCtrl and mseCtrlIn the mse package, Management Procedures (MPs) are modularly constructed using the mpCtrl class, which bundles together component controls (mseCtrl) for estimation, decision-making, and implementation.
mpCtrl| Component | Description | Example method |
Typical args |
|---|---|---|---|
est |
Estimator for stock status | perfect.sa, shortcut.sa |
metric, B0, devs, years |
hcr |
Harvest Control Rule | buffer.hcr, hockeystick.hcr, trend.hcr |
target, lim, trigger, metric, buffer |
phcr |
Parametrization of HCR (optional) | parametric.hcr, etc. |
Custom parameters |
isys |
Implementation system (e.g., allocation strategy) | split.is |
split = catch_props(...), noise, bias |
tm |
Technical measures (e.g., size limits, gear rules) | – | Optional, rarely used in CJM MSE setup |
mseCtrlAn mseCtrl object specifies how a module runs:
method: a function to apply (e.g., shortcut.sa)args: list of arguments passed to the methodmseCtrl(
method = buffer.hcr,
args = list(
target = 1000,
bufflow = 0.30,
buffupp = 0.50,
lim = 0.10,
min = 0,
metric = "depletion"
)
)You can modify or retrieve components using:
method(ctrl@hcr)
args(ctrl@hcr)$target <- 1200Or, using provided accessors:
hcr(ctrl) <- mseCtrl(method = new.hcr, args = list(...))
args(ctrl, "hcr")$target <- 1100This modular framework allows for flexible and transparent testing of MPs within the MSE simulation, including full customization of estimation, control rules, and implementation behavior. This notebook supports the JM MSE development process and is intended for use during scenario comparison, workshop reporting, and trade-off evaluation.
The Slick object is the core summary container created by the getSlick() and FLslick() functions. It contains performance data used for visualization and evaluation across multiple Management Procedures (MPs) and Operating Models (OMs).
| Slot | Contents |
|---|---|
@Boxplot |
MP × OM × performance indicators (boxplots) |
@Kobe |
SB/SBMSY vs F/FMSY over kobeyrs |
@Quilt |
Heatmap of average performance |
@Spider |
Scaled performance for visual trade-offs |
@Timeseries |
Time series of F, C, SB |
@Tradeoff |
Mean trade-off indicators (post-OM years) |
@MPs, @OMs |
Metadata: MP and OM definitions and labels |
# Load OM and compute baseline performance
h1 <- qread("data/h1_1.07.qs")
om <- iter(h1$om, seq(100))
omperf <- performance(om, years = 1970:2023, statistics = statistics[c("C", "F", "SB")])
perf <- readPerformance("demo/performance.dat.gz")
# Combine with MP results (perf), filtering to "tune" runs
sli <- getSlick(perf[grep("tune", run)], omperf, kobeyrs = 2034:2042)shorten_mp <- function(mpnames) {
gsub("h1_1.07_", "", mpnames) |>
gsub("cpue2", "C2", .) |>
gsub("cpue36", "C36", .) |>
gsub("cpue3", "C3", .) |>
gsub("scbad", "SCB", .) |>
gsub("scgood", "SCG", .) |>
gsub("scmedium", "SCM", .) |>
gsub("buffer", "BUF", .) |>
gsub("hcr_target_", "HCR_T", .) |>
gsub("tune", "T", .)
}
perf[, mp := paste0("H1_", shorten_mp(mp))]